多机器人协同探索3D环境并实时检测火灾，提升复杂场景下的自主导航能力。
Autonomous Multi-Robot Exploration Strategies for 3D Environments with Fire Detection Capabilitie
- 基于点云构建OctoMap地图，融合定位、建图与路径规划模块
- 采用势场法实现动态障碍物避障，保障多机器人安全导航
- 适用于消防搜救等需协同探索的现实场景，适合关注多机协作的研究者
本文系统综述了2D与3D环境中自主多机器人系统的探索策略，聚焦于建筑空间探索与火灾检测。针对依赖先验知识和预设地图的传统算法在环境变化时失效的问题，提出一种模块化方法，整合定位、建图与轨迹规划，基于点云数据生成OctoMap框架。探索策略通过势场法实现障碍物避让，确保在动态环境中的安全导航。此外，展望未来研究方向，包括去中心化地图构建、无人飞行器间协同探索以及对时变环境的适应性改进。本工作为提升多机器人协同探索算法的实际应用价值奠定基础。
原文摘要 · Abstract (English)
This paper presents a comprehensive overview of exploration strategies utilized in both 2D and 3D environments, focusing on autonomous multi-robot systems designed for building exploration and fire detection. We explore the limitations of traditional algorithms that rely on prior knowledge and predefined maps, emphasizing the challenges faced when environments undergo changes that invalidate these maps. Our modular approach integrates localization, mapping, and trajectory planning to facilitate effective exploration using an OctoMap framework generated from point cloud data. The exploration strategy incorporates obstacle avoidance through potential fields, ensuring safe navigation in dynamic settings. Additionally, I propose future research directions, including decentralized map creation, coordinated exploration among unmanned aerial vehicles (UAVs), and adaptations to time-varying environments. This work serves as a foundation for advancing coordinated multi-robot exploration algorithms, enhancing their applicability in real-world scenarios.
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